Iterative image search algorithm informed by continuous human-machine input feedback

ABSTRACT

System and computer-implemented method of analyzing tags associated with a sequence of images presented to a user to present a current interest of the user is disclosed. An image from among a plurality of images is presented on an electronic display. The image is associated with a set of tags. An input is received indicating a user&#39;s preference for the image. A plurality of tags is processed based on the preference and the set of tags to determine a next set of tags from the plurality of tags. A next image is determined from the plurality of images based on the next set of tags. The next image represents a physical object, different from a physical object represented by the previous image. A sequence of images is generated by repeating the above process with the next image in place of the previous image for present a user&#39;s current interest.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/688,362, filed Aug. 28, 2017, which is a continuation of U.S. patentapplication Ser. No. 15/070,371, filed Mar. 15, 2016, now U.S. Pat. No.9,779,160, which is a continuation of U.S. patent application Ser. No.14/827,205, filed Aug. 14, 2015, now U.S. Pat. No. 9,323,786, issuedApr. 26, 2016, which claims priority to and the benefit of U.S.Provisional Application No. 62/037,788, filed Aug. 15, 2014, all ofwhich are hereby incorporated by reference in their entireties.

FIELD OF THE PRESENT DISCLOSURE

Aspects of the present disclosure relate generally to systems andmethods of analyzing tags associated with a sequence of images presentedto a user to guide a user to a current interest.

BACKGROUND

There exist a multitude of applications, both Internet-basedapplications and smartphone/tablet-based applications, that makerecommendations for users based on analyzing a user's history. A user'shistory can include or reflect, for example, choices the user previouslymade based on the user's preferences. Although a user's preferences canbe constant as a whole over a long period of time, the generality of thepreferences allow for a user's current specific preference to be lessdefined. For example, user's preferences with respect to food are fairlyconstant. A user may prefer, for example, Italian food and Chinese food.A history of the user's food preferences captures this generalinformation and may allow applications to provide a generalizedrecommendation for what a user may want now or in the future. However, auser's current preferences can be more granular than what can becaptured by recommendation systems that rely on analyzing a user'shistory to make a current recommendation at a specific moment in timewhen the user may be craving something in particular. Thus, thegranularity of a user's current, specific preference also allows forspecific current interests than cannot accurately be predicted based ona user's history alone. For example, with respect to food, a user canexperience cravings—where a user desires a specific type of food amongall of the types of food that the user normally enjoys. Therefore,current applications do not help a user determine what the user'scurrent interest is despite the applications' having access to theuser's history. Moreover, although the user may know that he or shewants something, the particular object of the user's interest may beunknown even to the user until one of the user's senses is inspired orprovoked. Further, thinking by the user of what the users' currentinterest is, alone, may not help the user in defining his or her currentinterest.

According to aspects of the present disclosure, a system andcomputer-implemented method are disclosed that guide a user to his orher current interest based on a sequential presentation of imagesrepresentative of possible physical objects of interest.

SUMMARY

An aspect of the present disclosure includes a computer-implementedmethod of analyzing tags associated with a sequence of images presentedto a user in response to human-machine inputs made by the user topresent a current interest of the user, such as a food craving. Themethod includes presenting, via a display of an electronic device, oneimage from among a plurality of images. The one image represents aphysical object and is associated with one set of tags from a pluralityof tags. Each tag of the one set of tags describes or characterizesattributes of the physical object represented by the one image. Themethod further includes receiving, via a user interface of theelectronic device, an input by the user indicating a preference for thephysical object represented by the one image. The method also includesprocessing, by one or more computer devices, the plurality of tags basedon the preference and the one set of tags to determine a next set oftags from the plurality of tags. The method further includesdetermining, by the one or more computer devices, a next image from theplurality of images associated with the next set of tags. The next imagerepresents a physical object that is different from the physical objectrepresented by the one image, and the next set of tags describes orcharacterizes attributes of the physical object represented by the nextimage. The method also includes generating the sequence of images byrepeating the presenting, the receiving, the processing, and thedetermining with the next image in place of the one image during asession of presenting the current interest of the user.

An additional aspects of the present disclosure includes one or morecomputer-readable, non-transitory, storage media encodingmachine-readable instructions that, when executed by one or morecomputers, cause operations to be carried out. The operations includepresenting, via a display of an electronic device, one image from amonga plurality of images. The one image represents a physical object and isassociated with one set of tags from a plurality of tags. Each tag ofthe one set of tags describes or characterizes attributes of thephysical object represented by the one image. The operations includefurther include receiving, via a user interface of the electronicdevice, an input by the user indicating a preference for the physicalobject represented by the one image. The operations also includeprocessing, by one or more computer devices, the plurality of tags basedon the preference and the one set of tags to determine a next set oftags from the plurality of tags. The operations further includedetermining, by the one or more computer devices, a next image from theplurality of images associated with the next set of tags. The next imagerepresents a physical object that is different from the physical objectrepresented by the one image, and the next set of tags describes orcharacterizes attributes of the physical object represented by the nextimage. The operations also include generating the sequence of images byrepeating the presenting, the receiving, the processing, and thedetermining with the next image in place of the one image during asession of presenting the current interest of the user.

Further aspects of the present disclosure include a computer-implementedmethod of automatically recommending restaurants proximate a user basedon a sequence of the user's expressed food preferences. The methodincludes receiving a geographic location associated with the user, andretrieving, using one or more computer devices, a first set of digitalphotographs, each depicting a different food, and associated with aplurality of tags indicating a genre of the food featured in thecorresponding photograph, a description of the food featured in thecorresponding photograph, and a food establishment where thecorresponding photograph of the food was taken. The food establishmentis located within a predetermined proximity of the geographic location.The method further includes displaying, by a display device, the firstset of photographs over one or multiple frames on the display device.For each of at least some of the first set of photographs, the methodincludes receiving via a user input device one of at least two inputoptions, the at least two input options including a favorable indicationof a preference for the food featured in the corresponding one of the atleast some of the first set of photographs or an unfavorable indicationof a disinclination for the food featured in the corresponding one ofthe at least some of the first set of photographs. The method alsoincludes storing, for each of the at least some of the first set ofphotographs, in a memory storage device, a record indicating arelationship between the received input option and the tags associatedwith each corresponding one of the at least some of the first set ofphotographs, to produce a plurality of records on the memory storagedevice. The method further includes analyzing, using the computer oranother computer, the records to identify a genre of food having atleast a probable chance of being liked by the user, and retrieving,using the computer or another computer, a further digital photographdepicting a food, the further digital photograph being associated with atag indicating a genre of the food featured in the further photographand having at least a probable correlation with or matching theidentified genre. The method also includes using the received geographiclocation, the computer or another computer identifying at least one foodestablishment, which is located in a predetermined proximity to thereceived geographic location and which serves the identified genre offood, and displaying, by the display device, information regarding theat least one identified food establishment.

Additional aspects of the present disclosure will be apparent to thoseof ordinary skill in the art in view of the detailed description ofvarious embodiments, which is made with reference to the drawings, abrief description of which is provided below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram of a computer system according toan aspect of the present disclosure.

FIG. 2A is a flowchart of a computer-implemented method or algorithm ofanalyzing tags associated with a sequence of images presented to a userto present a current interest of the user according to aspects of thepresent disclosure.

FIG. 2B is a flowchart of a computer-implemented method or algorithm ofdetermining from among a pool of tags and a pool images tags and imagesthat are relevant for a user according to aspects of the presentdisclosure.

FIG. 2C is a flowchart of a computer-implemented method or algorithm ofdetermining and/or updating associations between elements within thesystem according to aspects of the present disclosure.

FIG. 3 is a diagram of a flow illustrating the processing of a pluralityof tags that are relevant to a user according to aspects of the presentdisclosure.

FIG. 4A illustrates a user interface of a computer-implemented method orprocess of analyzing tags associated with a sequence of images presentedto a user to present a current interest of the user according to aspectsof the present disclosure.

FIG. 4B illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4C illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4D illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4E illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4F illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4G illustrates another user interface of a computer-implementedmethod or process of analyzing tags associated with a sequence of imagespresented to a user to present a current interest of the user accordingto aspects of the present disclosure.

FIG. 4H illustrates a user interface for uploading an image according toaspects of the present disclosure.

FIG. 4I illustrates another user interface for uploading an imageaccording to aspects of the present disclosure.

FIG. 4J illustrates another user interface for uploading an imageaccording to aspects of the present disclosure.

FIG. 4K illustrates a user interface for visualizing a profile of a useraccording to aspects of the present disclosure.

FIG. 4L illustrates a user interface for visualizing a profile of a useraccording to aspects of the present disclosure.

DETAILED DESCRIPTION

While this disclosure is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail example implementations of the inventions and concepts hereinwith the understanding that the present disclosure is to be consideredas an exemplification of the principles of the inventions and conceptsand is not intended to limit the broad aspect of the disclosedimplementations to the examples illustrated. For purposes of the presentdetailed description, the singular includes the plural and vice versa(unless specifically disclaimed); the words “and” and “or” shall be bothconjunctive and disjunctive; the word “all” means “any and all”; theword “any” means “any and all”; and the word “including” means“including without limitation.”

A (software) module can refer to computer-readable object code thatexecutes a software sub-routine or program, which corresponds toinstructions executed by any microprocessor or microprocessing device toperform described functions, acts, or steps. Any of the methods oralgorithms or functions described herein can include non-transitorymachine or computer-readable instructions for execution by: (a) anelectronic processor, (b) an electronic controller, and/or (c) any othersuitable electronic processing device. Any algorithm, software module,software component, software program, routine, sub-routine, or softwareapplication, or method disclosed herein can be embodied as a computerprogram product having one or more non-transitory tangible medium ormedia, such as, for example, a flash memory, a CD-ROM, a floppy disk, ahard drive, a digital versatile disk (DVD), or other electronic memorydevices, but persons of ordinary skill in the art will readilyappreciate that the entire algorithm and/or parts thereof couldalternatively be executed by a device other than an electroniccontroller and/or embodied in firmware or dedicated hardware in awell-known manner (e.g., it may be implemented by an applicationspecific integrated circuit (ASIC), a programmable logic device (PLD), afield programmable logic device (FPLD), discrete logic, etc.).

As discussed above, there currently exist recommendation systems thatare unable to account for a user's current (i.e., contemporaneous)interest or preference based on the breadth of the user's interest,despite, for example, the recommendation systems having access toinformation regarding the user's history. The granularity of a user'scurrent preference relative to, for example, a user's historicalpreferences with respect to the subject matter relating to thepreference prohibits current recommendation systems from being able toestimate or present the user's current interest at the moment in timethe user may be craving something. At best, current recommendationsystems merely provide a one-time guess regarding the user's currentpreference. Moreover, within the realm of food, in particular, currentapplications exist that provide information regarding broad categoriesof food that are available within the user's current location. However,the amount of information provided by such applications may amount toinformation overload. The information overload does not allow a user todetermine a specific food dish in which the user is currentlyinterested. What is needed, inter alia, is a guided, iterative searchingsolution that repeatedly and dynamically adjusts searching criteria inresponse to human-machine inputs made by the user to arrive at arecommendation that will satisfy the user's contemporaneous craving atthe conclusion of the search session. In this way, both human andmachine are necessary partners in this search strategy. Only the humancan, using his or her subjective senses, ascertain from an image how auser feels about what is presented in the image. However, to satisfy animmediate craving, the machine is needed to help the human user arriveat a recommendation quickly, within less than a minute or so. TheInternet, cellular data and wireless technology, and smartphones havegiven users instant access and visibility to a myriad of options, but insome respects, this access and visibility is as much a blessing as it isa curse. With so many choices and options readily available, a newprogrammed machine is needed to help users find relevant informationquickly to satisfy needs that are fleeting and must be satisfiedquickly.

Accordingly, aspects of the present disclosure provide for systems andmethods of analyzing tags associated with a sequence of images presentedto a user to guide a user to a current interest. The systems and methodsallow for the presentation of a sequence of images to the user. Eachimage represents a physical object that the user can obtain through aphysical entity that provides the physical object. In the realm of food,for example, the physical object can represent a food dish and thephysical entity can represent a restaurant or store or foodestablishment (e.g., market, grocery, store, etc.) that offers the fooddish. A food dish as used herein includes any edible item or combinationof edible items that can be consumed (e.g., eaten or drunk) by animals,including drinks such as bubble tea, milkshakes, or cocktails. Eachimage within the sequence is associated with a set of tags that describethe physical object. Based on a user's preference for each tag, a newimage is presented to generate the sequence of images. The new image isselected based on the set of tags associated with the image thatdescribe the physical object represented by the image, and how the setof tags for the new image relate to the set of tags for the previousimage based on the preference of the previous image. The sequence ofpresenting images to the user that represent physical objects continuesto guide the user into determining a physical object that satisfies ordescribes the user's current interest. Once the user is presented withan image that represents a physical (tangible) object that the user iscurrently interested in (e.g., craving), the systems and methods allowfor the user to select the image (e.g., a digital photograph) to bepresented information on how to obtain (e.g., consume) items depicted inthe physical image. In the realm of food as a human interest, forexample, the user can be directed to an Internet web site of a physicalentity such as a restaurant or other food establishment that serves oroffers food at which the user can purchase the dish of food andoptionally consume at the food establishment. According to someembodiments, the user can be directed to an electronic user interface ofa software application, independent from the web site of the restaurant,at which the user can purchase the dish of food, to provide additionalconvenience to the process of obtaining the food dish.

FIG. 1 is a functional block diagram of a system 100 according to anaspect of the present disclosure. First, the general components of thesystem 100 will be introduced, followed by examples. The system 100includes one or more electronic computers (clients) 102 a, 102 b.Reference numbers used herein without a letter can refer to a specificone of the plurality of items, a subset of multiple items of theplurality of items, or all items of the plurality of items so numberedwith the same reference number. Thus, by way of example, the referencenumber 102 can refer to the computer 102 a, the computer 102 b, or bothof the computers 102 a and 102 b, as shown in FIG. 1. The one or morecomputers 102 a, 102 b connect to a communication network 104, such asthe Internet. However, the communication network 104 can be any type ofelectronic communication network. A computer as used herein includes anyone or more electronic devices having a central processing unit (CPU) orcontroller or microprocessor or microcontroller as understood by thoseskilled in the art of electronic computers. Examples of computersinclude tablet computers, laptop computers, desktop computers, servers,smartphones, a wearable electronic device such as a watch, an eyeglass,an article of clothing, or a wristband, and personal digital assistants(PDAs). The term computer as used herein can include a system ofelectronic devices coupled together to form what is conventionallyreferred to as a computer.

For example, one or more input devices, such as a keyboard or a mouse,and one or more electronic display devices, such as a video display, canbe coupled to a housing that houses the CPU or controller. Or, allcomponents of the computer can be integrated into a single housing, suchas in the case of a tablet computer or a smartphone. The one or morecomputers 102 a, 102 b conventionally include or are operatively coupledto one or more memory devices that store digital information therein,including non-transitory machine-readable instructions and data.

The one or more computers 102 a, 102 b include user interface devices110 a, 110 b. Each user interface device 110 a, 110 b corresponds to ahuman-machine interface (HMI) that accepts inputs made by a human (e.g.,via touch, click, gesture, voice, etc.) and converts those inputs intocorresponding electronic signals. Non-limiting examples of userinterface devices 110 a, 110 b include a touchscreen, a keyboard, amouse, a camera, and a microphone. These are also referred to ashuman-machine interface devices, because they allow a human to interactwith a machine by providing inputs supplied by the human user to themachine.

The one or more computers 102 a, 102 b also include electronic displaydevices 112 a, 112 b that are configured to display information that canbe visually perceived. Non-limiting examples of display devices 112 a,112 b include an electronic video display, a stereoscopic display, orany electronic display configured to visually portray informationincluding text, static graphics, and moving animations that isperceivable by the human eye. The electronic display devices 112 a, 112b display visual information contained in an electronic user interface(UI). The electronic UI can also include selectable elements that areselectable using the one or more HMI devices 110 a, 110 b. Thus, theelectronic UI generally can include a graphical user interface (GUI)component and a human-machine user interface component, via which ahuman user can select selectable elements displayed on the GUI via theHMI interface.

The one or more computers 102 a, 102 b also include softwareapplications 114 a, 114 b. That is, the one or more computers 102 a, 102b execute non-transitory machine-readable instructions and data thatimplement the software applications 114 a, 114 b. The applications 114a, 114 b perform one or more functions on the one or more computers 102a, 102 b. The applications 114 a, 114 b can be various specific types ofapplications, such as a web browser application or a native application.Within the system 100, the applications 114 a, 114 b convey informationbetween the one or more computers 102 a, 102 b and the communicationnetwork 104 (e.g., Internet) via a conventional wired or wirelesselectronic communications interface associated with the one or morecomputers 102 a, 102 b. Alternatively, or in addition, the applications114 a, 114 b can be a native application. Native applications conveyinformation between the one or more computers 102 a, 102 b over thecommunication network 104 to an application server 106. The nativeapplications 114 a, 114 b conventionally convey information between theone or more computers 102 a, 102 b over the communication network 104via a conventional wired or wireless electronic communications interfaceassociated with the one or more computers 102 a, 102 b.

As described above, the server 106 is also coupled to the communicationnetwork 104. The server 106 is a type of computer, and has a wellunderstood meaning in the art. The server 106 can be, for example, a webbrowser server, such as in the case of applications 114 a, 114 b beingweb browser applications. Or, the server 106 can be, for example, anative application server, such as in the case of applications 114 a,114 b being native applications.

An electronic database 108 is incorporated in or is coupled to theserver 106. The database 108 is a form of a memory device or a datastore, and stores electronic data for retrieval and archival relative tothe server 106. Both the server 106 and the one or more applications 114a, 114 b communicate information according to one or more protocols,such as the hypertext transfer protocol (HTTP) in the case of thecommunication network 104 being the Internet. In the case of thecommunication network 104 being a private local area network (LAN),instead of the Internet, any other communications protocol can be usedinstead of the HTTP. For example, native applications can insteadcommunicate using a proprietary or conventional communications protocolto pass information between the one or more computers 102 a, 102 b andthe server 106.

Although the system 100 is shown generally with respect to FIG. 1 asincluding two computers 102 a, 102 b, one server 106, and one database108, the system 100 can include any number of computers 102 a, 102 b,any number of independent or clustered servers 106 (e.g., server farm orsever cluster), and any number of databases 108. Moreover, some or allfunctionality of one or more components of the system 100 can betransferred, in whole or in part, to other components of the system 100.By way of example, functionality of the server 106 and/or the database108 can be transferred, in whole or in part, to the one or morecomputers 102 a, 102 b, depending on the functionality and performanceof the computers 102 a, 102 b.

The applications 114 a, 114 b communicate with the server 106 and thedatabase 108 over the communication network 104 for analyzing tagsassociated with a sequence of images presented to a user to guide a userto a current interest. The applications 114 a, 114 b control the userinterface devices 110 a, 110 b and the display devices 112 a, 112 b topresent the images to the user and to receive inputs from the userindicating the user's preferences for the images. The images arecommunicated over the communication network 104 to the applications 114a, 114 b of the one or more computers 102 a, 102 b from the database108, either directly or through the server 106. Accordingly, based on aclient-server arrangement of the system 100, with the computers 102 a,102 b as the clients and the server 106 as the server, the database 108stores the information used for analyzing tags associated with asequence of images presented to a user to guide a user to a currentinterest. The server 106 performs the functionality of the algorithmsdescribed herein, including serving the information from the database108 to the clients (e.g., computers 102 a, 102 b). The computers 102 a,102 b present the information to the user and receive the inputs fromthe users, which are then presented to the server 106 for processing.However, the functionality disclosed herein with respect to thedisclosed algorithms can be divided among the components of the system100 differently than as explicitly disclosed, without departing from thepresent disclosure. For example, all of the functionality disclosedherein can be embodied in one or more of the computers 102 a, 102 b,such as the computers 102 a, 102 b being arranged as a distributednetwork, depending on the capability of the computers 102 a, 102 b.

As one facet of the information, the database 108 electronically storesthe electronic images within a data store of images. The images can beof various file formats and image types. By way of example, the fileformats can include JPEG, Tagged Image File Format (TIFF), PortableNetwork Graphics (PNG), etc. The image types can include digitalphotographs, digital drawings, icons, etc. As discussed above, theimages stored on the database 108 represent a physical object that maybe of interest to the user (e.g., the user may be craving). Accordingly,the images visually convey information to the user so that the userunderstands the physical objects that the images represent. The system100 can initially include a set number of images. The set number of theimages can be defined by the administrator of the system 100. Asdescribed below, the system 100 also allows for users to add additionalimages to the system 100. For example, users can upload images from theone or more computers 102 a, 102 b to add additional images to thedatabase 108. As the users interact with the system 100, and the usersupload images to the system 100, the number of images increases. Forexample, the images can be digital photographs of food (includingdrinks) the users who upload them have ordered or seen at restaurantsthey have visited.

For each image, the database 108 stores information regarding thephysical object that the image represents. The physical object can beany physical (tangible) object that is representable by an image. By wayof example, and without limitation, the physical objects can be fooddishes, consumer goods, such as clothing, automobiles, etc., physicallocations, such as vacation spots, museums, sports venues, etc. Forpurposes of convenience, the present disclosure is related primarily tofood dishes as the physical objects. However, as understood by one ofordinary skill in the art, the disclosure is not limited to physicalobjects that are only food dishes. Rather, each physical object can beany physical object represented by an image such that a user canidentify the physical object when presented the image.

The database 108 also stores electronic tags. Primary tags are usedwithin the system 100 to describe and/or characterize the physicalobject that is represented by an image. The primary tags can includesingle words or several words linked together as a tag that describe orcharacterize the physical object overall, or that describe orcharacterize sub-components or sub-aspects (e.g., attributes) of thephysical object. Accordingly, for each image, the image is associatedwith a set of tags that describe or characterize the physical object.The database 108 stores all of the tags within a pool of tags, which isthe totality of tags that can be associated with an image to describe acharacteristic and/or a quality of the physical object that isrepresented by the image. The primary tags can be any type of descriptorof the physical object represented by the image. With respect to thephysical objects being food dishes, the tags can describe generalaspects of the food dishes, such as, for example, an ethnicity or acuisine of the food dish, such as African, American, Argentinian, etc.,which meal of the day the food dish generally applies to, such asbreakfast, brunch, lunch, dinner, supper, snack, dessert, late-night,etc. The tags can also be adjectives that describe the food dishes, suchas, for example, cheesy, crunchy, hot, cold, spicy, etc. The tags canalso identify the food or ingredients generally that constitute the fooddish, such as, for example, vegetables, meats, fruits, breads, etc. Thetags can also identify the food or ingredients more specifically thatconstitute the food dish, such as, for example, identifying the specificfruit, such as apples, apricots, etc., the specific meat, such as beef,poultry, fish, pork, etc., the specific vegetable, such as broccoli,asparagus, lettuce, carrots, etc. The tags can also identify thespecific food dish as a whole, such as, for example, spaghetti withvodka sauce, fettuccine stroganoff, leg of lamb, etc., or constituentsub-dishes (e.g., “sides”) within the food dish, such as, for example,sausage, eggs, pancakes, French toast, etc., that are all within thefood dish of a large breakfast with various constituents.

The tags can be objective, subjective (or semi-subjective), ortangential regarding how the tags describe or characterize the physicalobject that is associated with the image. The examples of primary tagsprovided above are objective tags that directly describe the physicalobjects. The tags may additionally include tags that are at leastpartially subjective and/or tangentially describe the physical objects.With respect to food dishes as the physical objects for purposes ofexample, subjective or semi-subjective tags may include, for example,tasty, mouth-watering, delicious, healthy, hearty, etc. Such subjectiveor semi-subjective tags may apply to the food dish for one user but notnecessarily all users. Tangential tags may describe aspects of thephysical object only when correlated with other information. Such otherinformation may only be known or apply to a subset of users thatinteract with the system 100. By way of example, such tags may be termscurrently trending in social media, such as hashtags on TWITTER® thatapply to only a subgroup of users that are following the current socialmedia trends. Such tags include, for example, hipster, yolo, GenY, GenX,etc. Independent of the context of the tag, these tangential tags do notnecessarily apply to a physical object. However, patterns may developthat allow certain tangential tags to be understood as referring to aquality or characteristic of a physical object.

Like the images, the system 100 initially begins with a certain numberof tags. However, the group of tags can be dynamic and evolve as theusers interact with the system 100. For example, additional tags can beadded to the pool of tags as users upload new images of physical objectsto the system 100 and describe the physical objects based on new tagsthat the users create. The users can create additional tags to describeor characterize the physical object that is associated with the imagethat the users uploaded. Each image is associated with one or more ofthe tags from among the group of tags as a set of tags for the image.The association can be based on an administrator of the system 100associating the tags with the images. Alternatively, or in addition, theassociation can be based on users of the system 100 associating the tagswith the images and/or creating new tags. The association based on theusers can be manual, such as the users manually selecting a tag toassociate with an image. Alternatively, or in addition, the associationcan be automatic, such as the system 100 automatically determining tagsthat apply to images. Based on the images being associated with multipletags as a set of tags, the database 108 also stores informationpertaining to specific sets of tags. A specific combination of tags is aset of tags. A single set of tags can describe multiple different imagesbased on the generality of each tag and an image being associated withany number of tags. The database 108 may include a data structure, suchas a table, to track the various sets of tags based on the variousassociations between tags and images within the database 108.

The database 108 also stores and tracks associations between elements ofthe system 100, such as between tags, between sets of tags, betweenimages and tags and/or sets of tags, between users and the elements,etc. The system 100 can associate a tag with an image based on the imagealready being associated with another tag, and both of the tagsincluding an association. By way of example with respect to food dishesas physical objects, an image may represent a Chinese food dish thatincludes rice. The image may already be associated with the tag Chinesebut not be associated with the tag rice. Based on an associationdeveloped by the system 100 tracking usage of the tag “Chinese” with thetag “rice,” in addition to, for example, other users liking other imagesthat are tagged with both the tag “Chinese” and the tag “rice,” thesystem 100 can automatically determine to associate the tag rice withthe image based on the image being associated with the tag Chinese. Theassociation can develop as the number of images that represent differentphysical objects increases within the database 108, or as more usersinteract with the tags and with the images. For example, as more usersupload images to the system 100, the users may associate both of thetags Chinese and rice to the newly uploaded images. The system 100tracks the continued association of the tag Chinese with the tag riceand logs the association within the database 108.

The tags may be divided into two overall categories, such as primarytags and secondary tags. Primary tags are defined by an administrator ofthe system 100. The primary tags include tags that directly describe orcharacterize the physical object. The system 100 at least initiallyincludes primary tags. The system 100 can also initially includesecondary tags. The secondary tags are defined by the administrator ofthe system 100 and/or by users of the system 100. The secondary tags mayidentify the same characteristic and/or quality as the primary tags, orthe secondary tags may identify different characteristics and/orqualities as the primary tags. Secondary tags may directly describe orcharacterize the physical object, such as with primary tags. Inaddition, secondary tags may subjectively describe or characterize thephysical objects, or may tangentially describe or characterize thephysical objects as described above.

Each image is associated with at least one primary tag, but can beassociated with any number of primary tags and secondary tags. Someimages may be associated with only one primary tag. For example, animage may represent the physical object bread and the only tagassociated with the image may be the tag bread. Some images may beassociated with many different tags, such as an image that representsthe physical object of French toast, which can include the tags bread,breakfast, egg, sweet, etc.

The systems and methods of analyzing tags associated with a sequence ofimages presented to a user to guide a user to a current interest relieson a plurality of tags that is associated with a user being processedbased on a previous set of tags of an image and a preference for thatimage from the user. Accordingly, the database 108 may store, or theserver 106 may dynamically generate, a plurality of tags that are asubset of all of the tags (e.g., pool of tags) that are stored on thedatabase 108. The plurality of tags includes not only the tags but alsothe sets of tags that correspond to the images that are covered by oneor more of the tags. As will be described in greater detail below, theplurality of tags may be tags that apply or are relevant to a user, suchas tags within the pool of tags that match tags associated with a user'sprofile. As the user is presented with images and provides preferencesin response to the images, the plurality of tags evolves as certain tagsare removed (or not considered) and/or certain sets of tags are removed(or not considered) from the plurality of tags.

The database 108 also stores user profiles. Generally, the user profilesinclude information that is used for the interacting with the system100. Such information can include certain tags indicated by the user toinclude with the user's profile, images, physical objects, and/orentities for which the user has indicated a positive or a negativepreference, independent of or dependent of the user interacting withimages presented to the user during a session of analyzing tagsassociated with a sequence of images presented to a user to guide a userto a current interest. With respect to food dishes for the physicalobjects, the information can include tags and images that apply to fooddishes that the user prefers (e.g., likes), and food dishes that theuser does not prefer (e.g., dislikes). The user can indicate suchpreferences through a manual selection of the tags. Alternatively, or inaddition, such preferences can be learned by the system 100 during theuser's interaction with the system 100 over a period of time, such asthrough an implicit selection of the tags as preferred tags through theuser indicating over time a preference for the tags. The preference canbe indicated according to a YES/NO schema, such as the user does or doesnot like a tag, an image, and/or a physical object. Alternatively, thepreference can be indicated according to a weighted schema, such as adegree to which the user does or does not like a tag, image, and/orphysical object. The profile information can include any otheradditional information associated with a user, such as the user's name,address, gender, age, ethnicity, religion, etc. The system 100 trackssuch additional information to mine trends across the users for tags,images, and/or physical objects. For example, the system 100 tracksuser's interactions within the system 100 to develop a user history. Theuser history tracks interactions between the user and the system 100 andallows the user to review the previous interactions. By way of example,the user history can include information pertaining to the user'spreference to specific images that were previously presented to theuser.

According to some embodiments, and with respect to food dishes as thephysical objects specifically, the user profiles can include dietaryrestrictions, such as, for example, gluten-free, vegan, vegetarian,religious observations (e.g., no pork or shellfish, or Kosher/Halalonly), nut allergies, etc. The database 108 can include dietaryinformation to automatically translate the entered dietary restrictionsinto negative preferences for certain tags, images, and/or physicalobjects to which the dietary restrictions apply so that restricted fooddishes are not presented to the user. The dietary information can belinked to the user's profile so that the user can view the dietaryinformation used by the system 100.

As discussed above, the physical objects represent objects that areoffered by various entities. The database 108 includes information withrespect to the location of the entity associated with the physicalobject and/or the image that represents the physical object. By way ofexample, the physical object can represent a food dish and the physicalentity represents the restaurant or store (e.g., market, grocery store,etc.) that offers the food dish. The database 108 includes informationwith respect to the location of the restaurant or store. In addition tothe location, the database 108 can also include entity profiles.According to some embodiments, the entity profiles can be organizedaccording to a subscription-based system. Entities that are subscribedto the system 100 can include specific information within their profilesthat entities who are not subscribed cannot. With respect to fooddishes, such information can include food dishes offered by the entity,such as images of food dishes that the entities created and uploadedinto the system 100, in addition to the menu, any currentspecials/promotions/discounts offered, etc. According to someembodiments, user interactions with the system 100 will allow for theusers to confirm the information presented in the entity profiles. Theentity profiles can also include images to showcase the entity'sphysical objects, additional links leading to their websites or socialmedia applications (e.g., FACEBOOK®, TWITTER®), etc.

The entity profiles allow users to browse the entities and click on asuggested or profiled entity, leading the user to the entity's profile.As part of the above-described associations, the system 100 collects andshares visitor frequency with entities when users are redirected to theentities' websites following selection of images associated withphysical objects that are associated with the entities. According tosome embodiments, the entity profiles will include a direct purchasinginterface for users, thereby obviating the need for users to seek thirdparty companies to order or consume physical objects associated with theentity.

FIG. 2A is a flowchart of a computer-implemented method or algorithm 200a of analyzing tags associated with a sequence of images presented to auser to guide a user to a current interest, using aspects of the presentdisclosure including the one or more computers 102 a, 102 b, the server106, and the database 108. The computer-implemented method or algorithm200 a may be executed within a computer 102 a, the server 106, thedatabase 108, or across multiple platforms, such as on the computer 102a and the server 106. In regard of the latter arrangement, anapplication 114 a executed by the computer 102 a (e.g., client-sideapplication) may perform the computer-implemented method or algorithm200 a in conjunction with an application executed on the server 106(e.g., server-side application) according to a client-serverrelationship. The computer-implemented method or algorithm 200 a beginswith a user initiating a session of the computer-implemented method oralgorithm 200 a. As will be described in greater detail below, thesession of the computer-implemented method or algorithm 200 a beginswith determining a plurality of tags that are associated with the userand that will be processed to determine subsequent images to present tothe user to generate a sequence of images during a session of thecomputer-implemented method or algorithm 200 a. The plurality of tagsalso determines the plurality of images from which the images that arepresented to the user are selected from. Thus, according to someembodiments, the computer-implemented method or algorithm 200 aprocesses only a subset of the tags and the images stored in thedatabase 108 based on the user that initiated the session of thecomputer-implemented method or algorithm 200 a. The computer-implementedmethod or algorithm 200 a begins with one of the images from among theplurality of images being presented to the user, such as through thedisplay device 112 a of the computer 102 a (202). As described above,the image represents a physical object and is associated with a set oftags. Each of tag of the set of tag describes the physical object thatis represented by the presented image. Thus, the user is presented withan image, and the user is able to recognize the physical objectrepresented by the image.

As will be also described below, along with the image, one or more userinterface elements or objects can be optionally presented on the displaydevice 112 a of the computer 102 a to allow the user to indicate apreference or inclination/disinclination for the physical object that isrepresented by the image. The user interface elements may vary dependingon the functionality/capability of the computer device 102 a, the userinterface device 110 a, and/or the display device 112 a. Alternatively,the display device 112 a may not present graphical user interfaceelements (although it could) specifically for the user indicating thepreference for the physical object. Rather or additionally, for example,it may be implicit what action the user should take to indicate thepreference, such as by swiping left on or near the image or anywhere onthe display device 112 a to indicate a negative preference (e.g.,dislike) and swiping right on or near the image or anywhere on thedisplay device 112 a to indicate a positive preference (e.g., like), orvice versa. To be clear, the present disclosure also contemplatesdisplaying graphical UI elements (e.g., like and dislike virtual buttonsdisplayed on the display device 112 for selection using a user interfacedevice 110), and recognizing gestures (e.g., swiping) made by a userrelative to a user interface device 110, or one or the other.

Upon the image being presented (e.g., displayed on the display device112) to the user, the computer-implemented method or algorithm 200 areceives an input from the user indicating a preference for the physicalobject represented by the image (204). The preference may be like or aninclination toward the object (e.g., positive) or dislike ordisinclination against the object (e.g., negative). Alternatively, thepreference may be like (e.g., positive), dislike (e.g., negative), orneither like nor dislike (e.g., neutral). A neutral preference mayindicate that the user cannot tell whether he or she likes or dislikesthe physical object represented by the image. Alternatively, thepreference may be scaled, such as a range of 1 to 10 to indicate thedegree that the user likes (e.g., 6 to 10) or dislikes (e.g., 1 to 5)the physical object represented by the image.

The computer-implemented method or algorithm 200 a then processes theplurality of tags based on the preference indicated by the user (206).Processing of tags refers to the manipulation or treatment of the tagsdrawn from the pool of tags by the computer 102 or server 106 during asession. The plurality of tags that is processed is the tags that areselected from the pool of tags stored within the database 108. Theprocessing includes determining a next set of tags based on thepreference the user provided in response to the previous image, and theset of tags that are associated with the previous image. Based on thepreference, the set of tags from the previous image determine how theplurality of tags is processed to determine the next set of tags. By wayof example, if the preference that the user indicated to a previousimage is positive, negative, or neutral, the plurality of tags areprocessed (e.g., treated) differently based on the set of tags of theprevious image. In response to the preference for the physical objectrepresented by the previous image being negative, the processing of theplurality of tags includes removing tags from the plurality of tags thatcorrespond to the tags from the set of tags of the previous image. Thetags are removed from the plurality of tags that are processed todetermine the next set of tags at each iteration of thecomputer-implemented method or algorithm 200 a, for the remainder of thesession, so that an image is not presented to the user for the remainderof the session that includes the particular tags. Although the tags aredescribed throughout as being removed from the plurality of tags,removal includes removing the tags from the plurality of tags and alsoincludes leaving the tags within the plurality of tags but notconsidering the tags. For example, the tags can remain within theplurality of tags but the tags can be marked as, for example, removedsuch that the tags are not considered during the processing of theplurality of tags.

As discussed above, the tags can be categorized generally as primarytags and secondary tags. An image can be associated with both primarytags and secondary tags. According to some embodiments, the tags thatare associated with an image and that are removed from the plurality oftags in response to a negative preference are only the primary tags fromthe set of tags associated with the image that received a negativepreference. Alternatively, both the primary tags and the secondary tagsfrom the set of tags associated with an image that received a negativepreference are removed from the plurality of tags in response to anegative preference. Alternatively, whether only primary tags or bothprimary tags and secondary tags are removed from the plurality of tagsmay be determined based on the number that have been presented to theuser. For example, if the image is one of the first N images presentedto the user, where N is 3, 4, or 5, only the primary tags associatedwith the image are removed from the plurality of tags in response to aninput by the user indicating a negative preference. However, if theimage is a later image presented to the user, such as the sixth,seventh, or eighth image, both the primary tags and the secondary tagsthat are associated with the image are removed from the plurality oftags. For subsequent images that are presented to a user after the userhas indicated a positive preference to at least one previous image, onlythose tags that are associated with the newly presented image and thatare new relative to the previously presented set of tags are the tagsthat are removed from the plurality of tags, whether they are onlyprimary tags or both primary and secondary tags.

In addition to removing tags (e.g., negative tags) that are associatedwith an image that the user provides a negative preference for, thecomputer-implemented method or algorithm 200 a may also remove tags thatare associated with the negative tags from the plurality of tags (e.g.,associated tags). As discussed above, in addition to the tags beingstored in the database 108, the database also stores associationsbetween tags. For example, certain tags may be associated with othertags based on trends in the physical objects represented by the images,such as the tag labeled “Chinese” being more associated with the taglabeled “rice” than the tag labeled “burger.” Accordingly, in responseto a negative preference, the computer-implemented method or algorithm200 a may determine associated tags that satisfy a threshold associationwith the negative tags. The threshold for the association may be basedon any number of factors or metrics, such as the number of times twotags are associated with the same image, the number of times a userindicates a certain preference (e.g., like or dislike) for an image, andthe tags that are associated with the image, etc. For example, theassociation may be based on the number of times two tags are associatedwith an image when the image is indicated by a user as having a positivepreference. However, the threshold for determining the association mayvary without departing from the spirit and scope of the presentdisclosure.

In response to the preference for the physical object represented by theimage being positive or favorable, the processing of the plurality oftags includes determining additional tags to add to the tags from theset of tags associated with the image. The additional tags furthernarrow down the current interest of the user by building upon the tagsassociated with the previous image, for which the user provided apositive preference.

To determine the one or more additional tags, the computer-implementedmethod or algorithm 200 a processes the plurality of tags to determinethe tags that have an association with the positive tags. By way ofexample, if the tags associated with the previous image include the tagsmeat and lunch, the computer-implemented method or algorithm 200 aprocesses the plurality of tags to determine tags that are associatedwith meat and bun, such as the tags hamburger or hot dog rather than,for example, the tag cereal. Similar to above, the threshold for theassociation may be based on any number of factors or metrics, such asthe number of times two tags are associated with the same image, thenumber of times a user indicates a certain preference (like or dislike)for an image, and the tags that are associated with the image, etc.However, the threshold for determining the association may vary withoutdeparting from the spirit and scope of the present disclosure.

Upon determining tags that satisfy a threshold association with one ormore tags from the set of tags that are associated with an image thatthe user indicated a positive preference for, the computer-implementedmethod or algorithm 200 a determines a next set of tags based on thenext set of tags including at least one tag of the tags having thethreshold association. Thus, if several tags are determined as having athreshold association with one or more tags that are associated with animage that the user indicated a positive preference for, one or more ofthose tags are selected and the next set of tags includes the one ormore tags of those tags and the previous tags of the previous image. Ifmore than one tag exists that satisfies the threshold association with atag from the previous set of tags, the selection of which of the one ormore tags to add to generate the next set of tags can vary. Theselection can be random, such that, for example, one or more tags from atotal of four tags are selected to be included in the next set of tags.Alternatively, the selection can be based on a weighting of the tags.The weighting can be determined based on one or more factors and/ormetrics. According to one metric, the weighting can be based on whichtag has the highest association with one tag, more than one tag, or allof the tags of the set of tags associated with the previous image thatthe user indicated a positive preference for. The association can berelative to all tags within the group or pool of tags, all tags that arerelevant to the particular user (e.g., that the user indicated apreference for), or all tags within the same category of tags, such asmeal, type of food, etc. However, the weighting can be based on any typeof metric or schema, such as based on a profile of the user, physicalobjects associated with the tags having a threshold association,locations corresponding to the physical objects associated with the tagshaving a threshold association, entities corresponding to the physicalobjects associated with the tags having a threshold association, or acombination thereof. Once the weighting of the tags is determined, theone or more tags that are added to the set of tags associated with theprevious image are selected such that the selected tags are the tagswith the highest weighting.

As discussed above, the tags can be categorized generally as primarytags and secondary tags. An image can be associated with both primarytags and secondary tags. According to some embodiments, only tags withinthe plurality of tags that are primary tags are processed to determineadditional primary tag(s) to add to the previous set of tags.Alternatively, all tags within the plurality of tags (e.g., primary andsecondary) are processed to determine the additional tags to be added tothe previous set of tags. Whether only primary tags are both primarytags and secondary tags are processed to determine additional tags toadd to the set of tags associated with the previous image can bedetermined based on the number of images within a sequence that alreadyhave been presented to a user. For example, if the image is one of thefirst N images presented to the user, where N is 2, 3, 4, or 5, only theprimary tags within the plurality of tags are processed to determine anadditional tag to add to the previous set of tags. However, if the imageis a later image presented to the user, such as the sixth, seventh, oreighth image within a session, both primary tags and secondary tags areprocessed to determine additional tags to add to the previous set oftags.

As discussed above, the preference that a user can indicate in responseto being presented an image representing an object can be a positivepreference or a negative preference. Additionally, the preference can bea neutral preference. In response to the preference indicated for animage being a neutral preference, the set of tags (e.g., combination oftags) that are associated with the image are logged and no set of tags(e.g., combination of tags) that include only that set of tags issubsequently presented to the user for the remainder of the session.Therefore, no image that is associated with a set of tags that includesonly that set is presented to the user for the remainder of the session.Alternatively, in response to the preference being a neutral preference,the set of tags that are associated with the image are logged and no setof tags that include that set of tags, in addition to any other tags, issubsequently presented to the user for the remainder of the session.Accordingly, in response to a neutral preference, thecomputer-implemented method or algorithm 200 a processes the pluralityof tags and determines the next set of tags based on the next set oftags not including the one set of tags corresponding to the image,either alone or, alternatively, in any combination with additional tags,for a remainder of the session. Thus, sets of tags are logged that areassociated with a neutral preference to narrow the possible next sets oftags and, therefore, images, that can be presented to the user.

In addition, according to some embodiments, and similar to thediscussion above with respect to a negative preference, the sets of tagsthat can be excluded from the possible next set of tags can include setsof tags that include tags that satisfy a threshold association with oneor more tags within the set of tags associated with a neutralpreference.

Upon determining the set of tags based on the preference and the set oftags for the previous image, the computer-implemented method oralgorithm 200 a determines the next image to present to the user (208).The next image is an image from a plurality of images that is associatedwith the next set of tags. Multiple images can be associated with thesame set of tags. Accordingly, the computer-implemented method oralgorithm 200 a the selects a single image from the images that sharethe same set of tags. The criteria of the selection of the image canvary. The selection can be random, such that a random image is selectedfrom the images that share the same set of tags. Alternatively, theselection may be based on a process or metric. As discussed above, thedatabase 108 stores information pertaining to how many times the userhas interacted with a specific tag and/or a specific image. The processor metric can include analyzing the number of interactions between theuser and the images that share the same set of tags and selecting theimage that has the highest number of interactions. The image with thehighest number of interactions may offer a high likelihood that the userhas a current interest in the physical object represented by the image.Alternatively, the process can include selecting the image that has thelowest number of interactions with the user. The interactions caninclude any interaction, such as any time the user was presented theimage and regardless of the preference the user provided in response tothe image. Alternatively, the interactions can be limited to onlyinteractions where the user provided a specific preference, such as apositive preference, a negative preference, or a neutral preference.Alternatively, the process or metric may be based on the entity that isassociated with the physical object that is represented by the image.Images that are associated with entities that have subscribed to thesystem 100 may be weighted higher than images that are associated withentities that have not subscribed to the system 100. Thus, an image maybe presented to a user, among multiple images that are associated withthe same set of tags, if the image is associated with a subscribingentity.

The computer-implemented method or algorithm 200 a generates a sequenceof images based on repeating the above process of at least presentingthe image to the user and awaiting an input from the user regardingwhether the user has a positive or negative, or neutral, preference forthe physical object represented by the next image (210). The session ofthe computer-implemented method or algorithm 200 a continues each timethe user provides a preference for the currently presented image, anddetermines the next set of tags and the next image to present based onthe preference for the previous image and the set of tags associatedwith the previous image. The user providing the preferences narrows downthe physical object that the user is currently interested in based onthe set of tags associated with the images presented to the user thatrepresent the various possible physical objects. Thecomputer-implemented method or algorithm 200 a continues within asession as long as the user continues to provide inputs corresponding tothe user's preferences to physical objects represented by images. Thus,according to some embodiments, the computer-implemented method oralgorithm 200 a continues indefinitely or at least until the number ofimages and/or tags are exhausted during the session based on theprocessing discussed above. During the session, tags and sets of tagsare removed based on negative or neutral responses, as described above.Thus, a session can end in the event that there are no more tags and/orimages to present to a user. Alternatively, a single session ofpresenting a sequence of images can last until a predetermined number ofimages have been presented or displayed to the user or until apredetermined number of inputs have been received from the user. Forexample, a session of presenting images can last for 10 images. If theuser has not yet determined a physical object that the user is currentlyinterested in after the 10^(th) image, the session ends, and thecomputer-implemented method or algorithm 200 a restarts a new session.Restarting a new session of the computer-implemented method or algorithm200 a resets the removed tags, the sets of tags that were neutralized,or both from the previous session. For example, all of the plurality oftags and sets of tags that were initially available at the beginning ofthe computer-implemented method or algorithm 200 a are again availablefrom which to determine new sets of tags and new images to present tothe user. Alternatively, the session of the computer-implemented methodor algorithm 200 a ends once the user selects an image that represents aphysical object that the user is interested in, as described in moredetail below.

FIG. 2B is a flowchart of a computer-implemented method or algorithm 200b of determining, from among the entire pool of tags and the entire poolimages, the plurality of tags and the plurality of images that arerelevant for the user for a particular session and that are processedand analyzed for determining images to present to the user, usingaspects of the present disclosure including the computer 102 a, theserver 106, and the database 108. The computer-implemented method oralgorithm 200 b can be a separate algorithm for purpose ofimplementation within the system 100 than the computer-implementedmethod or algorithm 200 a. Alternatively, the computer-implementedmethod or algorithm 200 b can be an extension or sub-routine of thecomputer-implemented method or algorithm 200 a.

The computer-implemented method or algorithm 200 b selects the pluralityof tags that are processed for determining the next set of tags,discussed above in the computer-implemented method or algorithm 200 a,and from which an initial image is presented, from among the pool oftags within the database 108 (212). The plurality of tags is selectedbased on the plurality of tags matching one or more tags associated witha profile of the user. Thus, tags that are relevant to a user, accordingto the tags matching tags that are within the user's profile, areselected to be within the plurality of tags that are processed asdiscussed above in the computer-implemented method or algorithm 200 a.The tags that are selected are tags that have an exact match with tagswithin a user's profile. Alternatively, the tags that are selected aretags that have an exact match or that satisfy a threshold associationwith tags within the user's profile. The association can be based on anyassociation described herein, such as the tags typically beingassociated with the same image based on trends of images and tags withinthe database 108. The tags that are selected from among the pool of tagscan be only primary tags, or the tags can be both primary tags andsecondary tags. In some examples, primary and secondary tags have anequal weight, but in other examples, a primary tag can have a higherweight compared to a secondary tag.

The computer-implemented method or algorithm 200 b also determines alocation associated with the user, the computer 102 a that is executingthe application to perform the computer-implemented method or algorithm200 b, or a combination thereof (214). The location associated with thecomputer 102 a can be determined automatically based on variousfunctionality of the computer 102 a, such as a GPS (global positioningsystem) receiver within the computer 102 a. Alternatively, the locationassociated with the user and the computer 102 a can be determined basedon the user manually entering a location within the computer 102 a. Thelocation manually entered by the user can be a current location or adifferent location, such as a location that a user plans on being atduring a certain time.

Based on the plurality of tags that are selected from the pool of tags,and the location of the user and/or the computer 102 a, thecomputer-implemented method or algorithm 200 b selects images from amongthe pool of images (216). The images are selected based on the imagesbeing associated with at least one tag of the plurality of tags that areselected from the pool of tags. Further, the images are selected basedon each image being associated with the location of the user and/or thecomputer 102 a. The images are selected based on the location because,as discussed above, the location corresponds to the location ofavailability of the physical object that is represented by the image.Based on the computer-implemented method or algorithm 200 b, theprocessing of the tags and the selection of the images within thecomputer-implemented method or algorithm 200 a is limited to the tagsthat are relevant to the user and to the images that represent physicalobjects that are local to a specific location (e.g., current geographiclocation of user and/or computer 102 a, or planned/expected location ofthe user and/or computer 102 a).

The first image presented during a session of the computer-implementedmethod or algorithm 200 a is an image within the pool of images. By wayof example, the first image is randomly selected from among theplurality of images as a first image of the sequence of images.Alternatively, the first image can be an image from among the pluralityof images that is associated with a high number of interactions with theuser, either through direct interactions between the user and the image,such as the user indicating a preference in response to being presentedan image, or through interactions between one or more tags associatedwith the image and the user. Alternatively, the first image presented tothe user within a session of the computer-implemented method oralgorithm 200 a can be based on a search of the user for a specific tag,physical object, or entity that provides the physical object. Forexample with respect to food, the user can elect to begin a session ofthe computer-implemented method or algorithm 200 a and enter the name ofthe physical object they seek, such as Chinese chicken salad. Inresponse, the computer-implemented method or algorithm 200 a searchesfor images within the database 108 containing one or more tagsdescribing or characterizing Chinese chicken salad, enabling the user tosearch their geographic area for restaurants/vendors bearing that fooditem. At the same time, this functionality encourages restaurants (e.g.,entities) associated with premium accounts to upload images of theirfood dishes, thereby making their food dishes searchable within thesystem 100 for all potential users within their local area.

FIG. 2C is a flowchart of a computer-implemented method or algorithm 200c of determining and/or updating associations between elements withinthe system 100, using aspects of the present disclosure including thecomputer 102 a, the server 106, and the database 108. Thecomputer-implemented method or algorithm 200 c can be a separatealgorithm for purpose of implementation within the system 100 than thecomputer-implemented method or algorithm 200 a and 200 b. Alternatively,the computer-implemented method or algorithm 200 c can be an extensionor a sub-routine of the computer-implemented method or algorithm 200 a.During a session of generating a sequence of images by thecomputer-implemented method or algorithm 200 a, the computer-implementedmethod or algorithm 200 c logs the inputs from the user as interactions(218). The inputs are logged as interactions with the tags, the sets oftags, the images, the physical objects, and/or the entities associatedwith the physical objects for which the inputs apply. When a userprovides an input of a preference associated with an image, the input islogged as applying to the image, the physical object represented by theimage, one or more tags associated with the image, and/or the entityassociated with the physical object. The input can be logged relative toonly the user, or the input can be logged across all users.

The logging of the inputs allows the computer-implemented method oralgorithm 200 c to modify associations between the various informationalelements within the system 100 (220). For example, the logging allowsthe computer-implemented method or algorithm 200 c to modifyassociations tags, between sets of tags, and/or between an image and atag and/or a set of tags based on the interactions. The associations canbe modified relative to the user making the inputs, or the associationscan be applied to all users. Accordingly, the associations discussed andused with the computer-implemented method or algorithms 200 a and 200 bare dynamic and constantly evolving based on the continued user inputs.

FIG. 3 is a diagram of a flow 300 illustrating the processing of aplurality of tags that are relevant to a user over the course of asession of the computer-implemented method or algorithm 200 a. The flowbegins with a set of tags 302. The set of tags 302 is associated with animage that is presented to the user at the computer 102 a throughexecution of the application 114 a. Specifically, the display device 112a displays the image that is associated with the set of tags 302. Asshown, the set of tags 302 includes primary tags 302 a and secondarytags 302 b. The tags can be any of the above-described tags; however,for purposes of convenience, the tags are represented by alphabeticalcharacters. Thus, the primary tags 302 a of the tag 302 include the tagsA, B, and C, and the secondary tags 302 b of the tag 302 include thetags D and E.

In response to the presentation of the image associated with the set oftags 302, the user indicates, for example, a preference for the physicalobject represented by the image. As described above, the preference maybe indicated through the user interface device 110 a. For purposes ofexplanation, the preference is represented by the arrow 314 a in FIG. 3.Specifically, the arrow 314 a represents a preference for the imageassociated with the set of tags 302 that is negative.

Based on the negative preference, the computer-implemented method oralgorithm 200 a processes the plurality of tags to determine a next setof tags and a next image that is associated with the next set of tags.The set of tags 304 represents the next set of tags determined by thecomputer-implemented method or algorithm 200 a, and an image that isassociated with the set of tags 304. The set of tags 304 includes theprimary tags 304 a F, G, and H, and the secondary tags 304 b I and J.Because the user provided a negative preference in response to thephysical object represented by the image that was associated with theset of tags 302, the set of tags 304 does not include any of the primarytags 302 a. Specifically, the primary tags 302 a of A, B, and C wereremoved from the plurality of tags that are processed to determine thenext set of tags for the remainder of the session of thecomputer-implemented method or algorithm 200 a.

Similar to above, in response to the presentation of the imageassociated with the set of tags 304, the user indicates a preference forthe physical object represented by the image. For purposes ofexplanation, the preference is represented by the arrow 316 a in FIG. 3.Specifically, the arrow 316 a represents a preference for the imageassociated with the set of tags 304 that is positive.

Based on the positive preference, the computer-implemented method oralgorithm 200 a processes the plurality of tags to determine a next setof tags and a next image that is associated with the next set of tags.The set of tags 306 represents the next set of tags determined by thecomputer-implemented method or algorithm 200 a, and an image that isassociated with the set of tags 306. Because the user indicated apositive preference to the previous physical object represented by theimage associated with the set of tags 304, the set of tags 306 includesthe primary tags 306 a F, G, H, and K, which are the primary tags 304 aand the additional primary tag K. That is, the computer-implementedmethod or algorithm 200 a builds upon the set of tags 304 based on thepositive preference of the user by determining a set of tags thatincludes the previous primary tags and an additional primary tag (ormore), and the corresponding image that the set of tags is associatedwith.

The set of tags 306 also includes the secondary tags 306 b D, E, and I.Despite the user indicating a negative preference for the set of tags302, which included the secondary tag D, the secondary tag D can be usedagain in a subsequent set of tags because the tag is a secondary tag.Alternatively, the secondary tags may also be removed from the pluralityof tags that are processed, for the remainder of the session, todetermine a next set of tags, instead of only the primary tags.

In response to the presentation of the image associated with the set oftags 306, the user indicates a preference for the physical objectrepresented by the image. For ease of explanation, the preference isrepresented by the arrow 314 b in FIG. 3. Specifically, the arrow 314 brepresents a preference for the image associated with the set of tags306 that is negative.

Based on the negative preference, the computer-implemented method oralgorithm 200 a processes the plurality of tags to determine a next setof tags and a next image that is associated with the next set of tags.The set of tags 308 represents the next set of tags determined by thecomputer-implemented method or algorithm 200 a, and an image that isassociated with the set of tags 308. The set of tags 308 includes theprimary tags 308 a F, G, H, and L, and the secondary tags 304 b M, N,and O. Because the user provided a negative preference in response tothe physical object represented by the image that was associated withthe set of tags 306, the set of tags 308 does not include the primarytag that was added between the set of tags 304 (e.g., last positivepreference) and the set of tags 306, i.e., primary tag K. That is, thenegative preference in response to the set of tags 306 is attributed tothe addition of the primary tag K; thus, the primary tag K is removedfrom the plurality of tags for the remainder of the session such that nosubsequent set of tags can include the primary tag K. The set of tags308 also includes the secondary tags 308 b M, N, and O.

In response to the presentation of the image associated with the set oftags 308, the user indicates a preference for the physical objectrepresented by the image. For purposes of explanation, the preference isrepresented by the arrow 318 a in FIG. 3. Specifically, the arrow 318 arepresents a preference for the image associated with the set of tags308 that is neutral.

Based on the neutral preference, the computer-implemented method oralgorithm 200 a processes the plurality of tags to determine a next setof tags and a next image that is associated with the next set of tags.The set of tags 310 represents the next set of tags determined by thecomputer-implemented method or algorithm 200 a, and an image that isassociated with the next set of tags 310. The set of tags 310 includesthe primary tags 310 a F, G, H, and P, and the secondary tags 310 b D,E, and O. Because the user provided a neutral preference in response tothe physical object represented by the image that was associated withthe set of tags 308, the set of tags 310 does not include the primarytag that was added between the set of tags 306 (e.g., last positivepreference) and the set of tags 308, i.e., primary tag L. That is, theneutral preference in response to the set of tags 308 is attributed tothe entire set of primary tags 308 a, including the primary tag K andthe primary tags F, G, and H. Thus, the set of primary tags 308 a isremoved from the plurality of tags that are processed, in the sense thatthe exact same set of primary tags 308 a can never be presented to theuser again. However, the primary tag L is not removed from the pluralityof tags for the remainder of the session such that subsequent sets oftags can include the primary tag L, as long as the set of tags is notthe exact set of primary tag 308 a. The set of tags 310 also includesthe secondary tags 310 b D, E, and O.

In response to the presentation of the image associated with the set oftags 310, the user indicates a preference for the physical objectrepresented by the image. For purposes of explanation, the preference isrepresented by the arrow 316 b in FIG. 3. Specifically, the arrow 314 brepresents a preference for the image associated with the set of tags310 that is positive.

Based on the positive preference, the computer-implemented method oralgorithm 200 a processes the plurality of tags to determine a next setof tags and a next image that is associated with the next set of tags.The set of tags 312 represents the next set of tags determined by thecomputer-implemented method or algorithm 200 a, and an image that isassociated with the set of tags 312. Because the user indicated apositive preference to the previous physical object represented by theimage associated with the set of tags 310, the set of tags 312 includesthe primary tags 310 a F, G, H, P, Q, and R, which are the primary tags310 a and the additional primary tags Q and R. That is, as describedabove, the computer-implemented method or algorithm 200 a builds uponthe positive preference of the user by determining a set of tags thatincludes the previous primary tags and one or more additional primarytags, and the corresponding image that the set of tag is associatedwith.

The flow 300 continues until the user selects a physical object that isrepresented by a currently presented image, which corresponds to thelast image that is presented within the above sequence of images, is aphysical object that the user would like to obtain. Alternatively, theflow 300 continues until the session is ended and restarted, for thereasons discussed above. In each case, when a session of thecomputer-implemented method or algorithm 200 a is started or restarted,the plurality of tags and images that are processed are reset such thatthe tags and sets of tags that were removed from the plurality of tagsare inserted back into the plurality of tags for processing.

The following figures use any of the aspects described above inconnection with the foregoing FIGS. 1-3. These figures and accompanyingdescription lay out some of the foundational aspects of the presentdisclosure, which the following figures show as mere exemplars of themany implementations contemplated by the present disclosure.

FIGS. 4A-4G illustrate user interfaces (UIs) 400 a-400 g, respectively,that are presented on a computer device 102 a as part of an application114 a executed on the computer device 102 a for analyzing tagsassociated with a sequence of images presented to a user to guide a userto a current interest. Referring to FIG. 4A, when starting a session ofthe computer-implemented method or algorithm, images appear one at atime and users interact with the images to populate a dynamic sequenceof images. According to the various configurations of the system 100,the images are pushed by the server 106, from the database 108, to theapplication 114 a on the computer 102 a. Alternatively, the images maybe retrieved from the database 108 by the application 114 a, eitherdirectly or through the server 106. Alternatively, the images may becontained within the application 114 a on the computer 102 a. FIG. 4Aillustrates the main UI 400 a of the computer-implemented method oralgorithm 200 a. As shown, the UI 400 a includes an image 402 a. Theimage 402 a represents a physical object. In the specific example ofFIG. 4A, the image 402 a is a digital photograph of loaves of bread,which represents the physical object of bread and could have been takenby someone perusing a store or restaurant serving these loaves of breadand posted to an online social media networking service, for example.Below the image 402 a are user interface elements 404 a and 404 b.Specifically, the user interface elements 404 a and 404 b allow a userto enter inputs associated with the image 402 a, and the correspondingphysical object represented by the image, for the user to provide apreference for the physical object represented by the image 402 a. Forexample, the user interface element 404 a is an icon of an X, whichcorresponds to a negative preference, and the user interface element 404b is an icon of a checkmark, which corresponds to a positive preference.The UI 400 a also includes a main toolbar 406 that allows the user tonavigate within the application 114 a. Within the main toolbar 406 areicons corresponding to functions of the application 114 a, including aFeed icon 406 a, a Restaurants icon 406 b, a Crave Search icon 406 c,and a My Profile icon 406 d. The Crave Search icon 406 a initiates asession of the computer-implemented process or algorithm 200 a thatbegins the process or algorithm for analyzing tags associated with asequence of images presented to a user to present a current interest ofthe user. Thus, prior to the UI 400 a being presented in the displaydevice 112 a or the computer 102 a, the user, for example, selected theCrave Search icon 406 c.

FIG. 4B shows a subsequent user interface after the UI 400 a.Specifically, upon the user selecting one of the user interface elements404 a or 404 b, the UI 400 a transitions to UI 400 b. UI 400 b includesa new image 402 b. Like image 402 a, the image 402 b represents aphysical object. In the specific example of FIG. 4B, the image 402 b isa digital photograph of beef stir-fry, which represents the physicalobject of a food dish of beef stir-fry. By way of example, the user mayhave selected the user element 404 a to indicate that the user has anegative preference (e.g., dislike) for the physical object of breadrepresented by the image 402 a. In response, the computer-implementedmethod or algorithm 200 a determined a next set of tags that does notinclude tags from the previous set of tags associated with the image 402a, and determined an image (e.g., image 402 b) that is associated withthe next set of tags to present to the user. Accordingly, the image 402b of the beef stir-fry does not have the same primary tags as theprimary tags associated with the image 402 a of the loaves of bread.

FIG. 4C shows a subsequent user interface after the UI 400 b. The UI 400c of FIG. 4C may be after several rounds of the computer-implementedmethod or algorithm 200 a selecting next sets of tags and imagesassociated with the next sets of tags, and receiving inputs from theuser indicating preferences for the physical objects represented by theimages. By way of explanation, the UI 400 c may be presented after an Nnumber of images were previously presented. Thus, the UI 400 c includesa new image 402 c. Like images 402 a and 402 b, the image 402 crepresents a physical object. In the specific example of FIG. 4C, theimage 402 b is a digital photograph of sushi, which represents thephysical object of a dish of sushi.

FIG. 4D shows a detailed view UI 400 d associated with the image 402 cin FIG. 4C. By way of example, the UI 400 c transitions to the UI 400 dby the user selecting the image 402 c in the UI 400 c. The UI 400 dincludes the same image 402 c in FIG. 4C. In addition, the UI 400 dincludes user interface elements 408 a and 410 a. User interface element408 a corresponds to a title or caption associated with the image 402 c.The title or caption of the user interface element 408 a is a textstring that describes the physical object that is represented by theimage 402 c. By way of example, where the image 402 c shows sushi, theuser interface element 408 a includes the caption California sushi roll.User interface element 410 a lists the tags that are associated with theimage 402 c. The user interface element 410 a allows a user to directlysee the tags that are associated with the image 402 c and, therefore,also associated with the physical object that is represented by theimage 402 c. By way of example, the user interface element 410 aincludes the tags sushi, roll, raw, Japanese, vegetable, and cold.

FIG. 4E shows a UI 400 e that includes a recommendation for a restaurantbased on the currently presented image 402 d, which is presented basedon the computer-implemented process or algorithm 200 a. For example, theuser may have indicated a negative preference or dislike in response tobeing presented the image 402 c. Based on reverting back to the tagsthat were last associated with a positive response (for example, 402 b),the computer-implemented process or algorithm 200 a may have determinedthe image 402 d as the next image to present to the user. Similar to theUI 400 d, the UI 400 e includes a detailed view associated with thecurrently presented image 402 d. As shown, the image 402 d shows adigital photograph of pork chops, which is indicated by the userinterface element 408 b. Specifically, the user interface element 408 bshows the caption of Pork Chops with Ripieno. The UI 400 e furtherincludes the user interface element 410 b, which provides the tags thatare associated with the image 402 d. As shown, the tags include herbs,wok, breadcrumbs, pork chops, and dinner. Further, the UI 400 e includesthe user interface element 412. The user interface element 412 providesan indication of a number entities within the area, for example, definedby the location of the user, the computer 102 a, or both, that offer thephysical object that is associated with the image 402 a. Specific to theillustrated example, the user interface element 412 indicates the numberof restaurants within the location of the user that offer the food dishthat is represented by the image 402 d. As shown, there are 15restaurants within a threshold location of the user that offer the fooddish of pork chops with ripieno that is associated with the image 402 d.

The user interface element 412 may be presented to the user within theUI 400 e in response to the user providing an indication that the useris interested in obtaining the physical object associated with the image402 d. Such an indication can be provided according to various methods,such as the user double tapping or selecting the image 402 d. Inresponse, the application 114 a presents the user interface element 412within the UI 400 e. Thus, once a user is guided to a physical objectthat the user is interested in, such as craving in the case of a fooddish, the computer-implemented process or algorithm 200 a allows theuser to obtain information on entities that offer the physical object.In this case, the entities are the 15 restaurants. In response to theuser selecting the user interface element 412, the application 114 acauses a transition between the UI 400 e and the UI 400 f.

FIG. 4F shows the UI 400 f, which includes a list of recommendedentities that offer the physical object that the user indicated as beinginterested in obtaining additional information in. The UI 400 f includesa list of entities 414 a-414 g, specifically restaurants, that offer thephysical object associated with the image 402 d. From the UI 400 f, theuser is able to choose a specific entity from the list of entities 414a-414 g. Upon selecting an entity, such as the first entity associatedwith the first user interface element 414 a, the application 114 acauses a transition between the UI 400 f to the UI 400 g.

FIG. 4G shows a UI 400 g that provides information regarding a specificentity that offers the physical object associated with the image 402 d.In the case of a restaurant, the UI 400 g includes an image 416 of theexterior of the restaurant. Below the image 416, the UI 400 g includesthe name of the restaurant and the address of the restaurant. The UI 400g also includes a user interface element 418. The user interface element418 can be associated with a hyperlink to a website that is associatedwith the restaurant. Alternatively, the user interface element 418 maydirect the user to a landing site within the application for restaurant.The landing site for the restaurant may provide information regardingthe restaurant, such as the information that is contained with therestaurant profile stored within the database 108. According to someembodiments, the landing site for the restaurant may allow a user topurchase the food dish through the application 114 a, rather than beingdirected to an Internet web site from which the user can purchase thefood dish.

FIG. 4H shows a UI 400 h for a user to upload and associate an image ofa physical object into the system 100, such as stored within thedatabase 108 of the system 100. The UI 400 h includes an area 420 todisplay the image that will be associated with the physical object. Theimage can be obtained according to various methods, such as a cameraintegrated within the computer 102 a, such as in the case of asmartphone, or by linking to an image on the Internet or saved in amemory device of the computer 102 a. Similar to the UI 400 f, the UI 400h also includes a user interface element 422 that allows a user toinsert a title or a caption for the image. The UI 400 h also includes auser interface element 424 to associate the image within the area 420with one or more tags that describe the physical object represented bythe image. The UI 400 h also includes a user interface element 426 toassociate a location with the image and the physical object. Thelocation can either be determined automatically, such as throughcomponents and/or modules within the computer 102 a (e.g., GlobalPositioning System receivers), or can be manually entered by the user.

FIG. 4I illustrates the UI 400 i as the user enters information withinthe user interface elements 422 and 422. For example, the user may beuploading an image of a food dish. The food dish may specifically bebeef stir-fry. The user may have selected the tags hot, wok, and Chineseto describe the food dish (e.g., physical object) represented by theimage. To select the tags and enter the caption, the UI 400 i includes avirtual keyboard 428. However, the user can enter the text according tovarious other user interface devices. Once the user has completedentering the information, the image is uploaded to the database 108.FIG. 4J shows the UI 400 j that includes the user interface element 430,which indicates a successful upload of the image and the associatedinformation, such as the tags, the caption, and the location.

FIGS. 4K and 4L show user interfaces associated with a user viewingaspects of the user's profile. Specifically, FIG. 4K shows UI 400 kassociated with a user profile, including the tags that the user isassociated with based on the user interface element 432. As shown, theuser profile indicates that the user is associated with the tags Talian,Asian, Burger joints, Romantic, Delivery, Music, Healthy food, Fresh,among other tags. The tags are associated with the user by the usermanually selecting certain tags from all of the tags that are stored inthe database 108 that the user has a positive preference for (e.g.,likes). Alternatively, the tags are associated with the user implicitlyby the user's interaction with the tags over time, such as the userhaving the habit of selecting images and/or physical objects that theuser prefers that are associated with the tags in the user interfaceelement 432. The UI 400 k also includes images (e.g., digitalphotographs) that the user has provided a positive preference for (e.g.,liked) during interaction with the application 114 a.

FIG. 4L includes the UI 400 l that shows at least some additionalaspects of the user's profile. For example, the UI 400 l includes userinterface elements 436 and 438. The user interface element 436 includessocial media information regarding the user, such as an icon of theuser, the user's location (e.g., city and state), and how many users theuser is following and are following the user. The user interface element438 includes images that the user has uploaded into the system 100, suchas through the flow illustrated in FIGS. 4H-4J. The UI 400 l can includeother information within the system 100, not just the informationspecifically referenced herein with respect to FIGS. 4A-4L. For example,the UI 400 l can include a feature for users called The Top 100, whichhighlights the top rated images and/or physical objects associated withthe images at any given moment. The Top 100 images can be out of all ofthe images stored within the database 108, all of the images within thedatabase 100 that are relevant to the user based on the images beingassociated with tags that the user has liked, or all of the images ofphysical objects that are offered within a pre-defined area surroundingthe user's current location. The images representing the physicalobjects with the most positive preferences will be featured on The Top100, which provides users the incentive to upload physical objects(e.g., such as food dishes) that are their favorite physical objects.For example, this will encourage users to share their best meals in thehopes of being in The Top 100. The Top 100 list allows users to gainattention. In the social media realm, the applications 114 a, 114 ballow users to attract more followers as well as support, for example,their favorite restaurants (e.g., entities). The Top 100 lists will alsobe active, allowing user to select an image within The Top 100 list tobe taken to a page associated with a profile of the entity that offersthe physical object represented by the image.

While this disclosure is susceptible to various modifications andalternative forms, specific embodiments or implementations have beenshown by way of example in the drawings and will be described in detailherein. It should be understood, however, that the disclosure is notintended to be limited to the particular forms disclosed. Rather, thedisclosure is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the invention(s) as defined bythe appended claims.

Each of these embodiments, and obvious variations thereof, iscontemplated as falling within the spirit and scope of the claimedinvention(s), which are set forth in the following claims. Moreover, thepresent concepts expressly include any and all combinations andsub-combinations of the preceding elements and aspects.

What is claimed is:
 1. A computer-implemented image search methodfiltered by multiple human-machine inputs on images presented to a userof the image search method, the method comprising the steps of:determining a plurality of different digital images to present on thevideo display device to generate a sequence of digital images, each ofthe digital images being associated with a plurality of tags indicatingone or more attributes of an image featured in the corresponding digitalimage; receiving, via a user input device, an unfavorable indication ofa disinclination for the image features in the one of the digitalimages, the unfavorable indication being one of at least two inputoptions, the at least two input options including, among the unfavorableindication, a favorable indication of a preference for the imagefeatured in one of the digital images; analyzing at least some of thetags to determine a next set of tags associated with a subsequentdigital image; adjusting weights of at least some of the tags based onan association relative to tags within the same category of tags towhich the subsequent digital image belongs; transitioning the one of thedigital images with the subsequent digital image on the video displaydevice to replace the one of the digital images with the subsequentdigital image on the video display device; and receiving via the userinput device a further input corresponding to one of the at least twoinput options including a favorable indication for the subsequentdigital image and an unfavorable indication for the subsequent digitalimage.
 2. The method of claim 1, wherein the corresponding imagefeatured on respective ones of the at least some of the digital imagesare consumer goods, food dishes, clothing, automobiles, physicallocations, vacation spots, museums, or sports venues.
 3. The method ofclaim 1, further comprising: displaying on the video display devicegraphical user interface elements corresponding to a like virtual buttonand a dislike virtual button, wherein the like virtual button whenselected indicates the favorable indication and wherein the dislikevirtual button when selected indicates the unfavorable indication. 4.The method of claim 1, in response to receiving the favorable indicationfor the image featured in the one of the digital images, increasingweights of at least some of the tags associated with the one of thedigital images based on an association relative to tags within the samecategory of tags to which the subsequent digital image belongs.
 5. Themethod of claim 4, wherein a subsequent digital image to be presented inresponse to the favorable indication is required to have at least someof the tags associated with the one of the digital images.
 6. The methodof claim 5, wherein the subsequent digital image in response to thefavorable indication is required to have all of the tags associated withthe one of the digital images.
 7. The method of claim 1, furthercomprising processing at least some of the tags differently in responseto the favorable indication versus the unfavorable indication.
 8. Themethod of claim 7, wherein the processing is based on a number of timesa user indicates a preference for the one of the digital images.
 9. Acomputer-implemented image search method, comprising the steps of:determining a plurality of digital images to present on a video displaydevice of a computer, each of the digital images being associated with aplurality of tags; repeating, as long as human-machine inputs arereceived from a user input device indicating a like or dislikepreference of a user for a selected digital image of the digital images,the steps of: processing the tags associated with the selected digitalimage to determine a next set of tags associated with a subsequentdigital image to be presented on the video display; in response to thedislike preference, the processing including adjusting weights of atleast some of those of the processed tags in the same category as thetags associated with the selected digital image so that the subsequentdigital image has a different set of associated tags from thoseassociated with the selected digital image, and replacing the selecteddigital image with the subsequent digital image on the video displaydevice; in response to the like preference, the processing includingadjusting weights of at least some of the processed tags so that thesubsequent digital image has at least some of the same tags as thoseassociated with the selected digital image, and presenting thesubsequent digital image on the video display device; wherein therepeating causes the image search method to iteratively resolve towardat least one digital image presented on the video display device, whichsatisfies a subjective need of the user via the multiple human-machineinputs, and the repeating includes at least one like preference and atleast one dislike preference.
 10. The method of claim 9, wherein inresponse to the like preference, the subsequent digital image isrequired to have all of the tags associated with the selected digitalimage.
 11. A computer-implemented image search method, comprising thesteps of: determining a plurality of digital images to present on avideo display device of a computer, each of the digital images beingassociated with a plurality of tags; repeating, as long as human-machineinputs are received from a user input device indicating one of at leasttwo input options for a selected image of the digital images, the atleast two input options including a favorable indication of a preferencefor the image featured in one of the digital images and an unfavorableindication of a disinclination for the image featured in the one of thedigital images, the steps of: processing the tags associated with theselected digital image to determine a next set of tags associated with asubsequent digital image to be presented on the video display; inresponse to the unfavorable indication, the processing includingadjusting weights of at least some of those of the processed tags in thesame category as the tags associated with the selected digital image sothat the subsequent digital image has a different set of associated tagsfrom those associated with the selected digital image, and replacing theselected digital image with the subsequent digital image on the videodisplay device; in response to the favorable indication, the processingincluding adjusting weights of at least some of the processed tags sothat the subsequent digital image has at least some of the same tags asthose associated with the selected digital image, and presenting thesubsequent digital image on the video display device; wherein therepeating causes the image search method to iterate toward at least onedigital image presented on the video display device, which satisfies asubjective need of the user via the multiple human-machine inputs, andthe repeating includes at least one favorable indication and at leastone unfavorable indication.